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  • Title: Risk stratification of adrenal masses by [18 F]FDG PET/CT: Changing tactics.
    Author: Salgues B, Guerin C, Amodru V, Pattou F, Brunaud L, Lifante JC, Mirallié E, Sahakian N, Castinetti F, Loundou A, Baumstarck K, Sebag F, Taïeb D.
    Journal: Clin Endocrinol (Oxf); 2021 Feb; 94(2):133-140. PubMed ID: 32978795.
    Abstract:
    CONTEXT: [18 F]FDG PET/CT improves adrenal tumour characterization. However, there is still no consensus regarding the optimal imaging biomarkers of malignancy. OBJECTIVES: To assess the performance of Tumour standardized uptake value (SUV)max :Liver SUVmax for malignancy-risk and to build and evaluate a prediction model. DESIGN/METHODS: The cohort consisted of consecutive patients with adrenal masses evaluated by [18 F]FDG PET/CT. The gold standard for malignancy was based on histology or a multidisciplinary consensus in nonoperated cases. The performance of the previously reported cut-off for Tumour SUVmax :Liver SUVmax (>1.5) was evaluated in this independent cohort. Additionally, a predictive model of malignancy was built from the training cohort (previous study) and evaluated in the validation cohort (current study). RESULTS: Sixty-four patients were evaluated; 28% of them had a Cushing's syndrome. Fifty-four adrenal masses were classified as benign and 10 as malignant (including 7 adrenocortical carcinomas). Compared to benign masses, malignant lesions were larger in size, had higher unenhanced densities and higher [18 F]FDG uptake. CT-derived anthropometric parameters did not differ between benign and malignant masses. A tumour SUVmax :Liver SUVmax  > 1.5 showed a good diagnostic performance: Se = 90.0%/Sp = 92.6%/PPV = 69.2%/NPV = 98.0% and accuracy = 92.2%. A predictive model based on tumour size and tumour-to-liver uptake SUVmax ratio for malignancy-risk was validated and provides a complementary approach to the ratio. CONCLUSIONS: Tumour SUVmax :Liver SUVmax uptake ratio is a useful biomarker for diagnosis of adrenal masses. Another tactic would be to calculate with the model an individual risk of malignancy and integrate this information into a shared decision-making process.
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